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"Diet is one of the most well-established lifestyle factors influencing longevity and health, as demonstrated by laboratory studies, clinical trials, and large-scale biomedical analyses. However, diet effects on aging are highly context dependent, shaped by multiple environmental and genetic factors. We currently lack both the knowledge and a robust framework to predict the outcomes of dietary interventions at the individual level. To capture this biological complexity and guide public health strategies, extensive datasets and robust quantitative models are urgently needed.
To investigate the role of diet in aging, we propose to use Drosophila melanogaster, a model uniquely suited for this goal. The Drosophila Genetic Reference Panel (DGRP) enables large-scale analysis of natural genetic variation, and recent advances in high-throughput feeding technologies—such as the Fly Liquid-Food Interaction Counter (FLIC)—allow precise quantification of individual eating behaviors, enabling analysis of a key behavioral gap that may link diet to age-related outcomes. Our program complements ongoing efforts in mammalian models by leveraging the genetic tractability, scalability, and short lifespan of Drosophila, enabling rapid identification of novel variants and mechanisms that mediate diet–aging interactions.
Our immediate goals are to investigate the eating behaviors most likely to contribute to variable outcomes under pro-longevity diets. Although numerous studies on pro-longevity diets have focused on modifying dietary composition—such as caloric restriction, macronutrient ratio, and dietary supplementation—animals’ feeding behaviors are inherently different and are altered in response to these interventions. Critically, individual behavioral responses to dietary manipulation remain understudied, despite growing evidence that the biological context of eating—including how, when, and how frequently organisms eat—may significantly influence health and longevity.
Specifically, this summer project aims to identify key eating parameters that determine diet-induced longevity outcomes. We and others have recently reported variable responses to the well-established lifespan-extending effects of dietary restriction in Drosophila. We hypothesize that undocumented differences in feeding behavior may underlie this heterogeneity in dietary responses. Using a subset of DGRP lines (100 strains), we will measure lifespan and detailed feeding parameters—including meal timing, frequency, duration, and size—under two pro-longevity dietary interventions (protein restriction and lithium supplementation). We will integrate a linear mixed-effects model (LMM) with two machine-learning approaches to identify specific eating behaviors that predict beneficial or detrimental effects on lifespan and metabolic health.
Successful completion of this summer project will establish a high-throughput and reliable experimental dataset linking eating behavior to longevity, and will develop machine-learning algorithms capable of predicting lifespan based on behavioral profiles. This integrative approach will strengthen our broader research program, which aims to combine machine-learning models with our high-throughput experimental platforms to address key quantitative questions in aging and longevity biology."
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